AI is already changing how prospective students discover universities, compare programs, and decide where to apply. The shift is simple: students are moving from browsing to asking. They want direct answers, clear pathways, and decision support—without having to interpret a complex institutional structure.

This isn’t speculative adoption. In HEPI’s Student Generative AI Survey 2025, the proportion of students using generative AI tools for assessments jumped from 53% (2024) to 88% (2025).

For leaders responsible for enrolment, communications, digital transformation, IT, and student services, this creates both risk and opportunity. The risk is losing influence over early decision-making as AI tools become the first interface layer. The opportunity is using AI as a catalyst to fix long-standing friction points and create a clearer, more supportive student experience end-to-end.

At PH1 (ph1.ca), we help large, complex institutions make digital experiences clearer, more consistent, and easier to navigate—so students and stakeholders can get the answers they need with confidence. With a focused inquiry on AI and how it’s reshaping discovery, decision-making, and service delivery, we help higher education institutions strengthen recruitment, improve the student experience, and ensure institutional information remains trusted and authoritative in an AI-mediated world.

Why this matters now

Most institutions are facing multiple shifts at once. AI is not one change—it’s a multiplier that affects search behavior, trust, and service expectations.

• Discovery is changing. In UPCEA + Search Influence’s 2025 research, 50% of prospects use AI tools at least weekly, and 24% use them daily.

• Decision-making is accelerating. Students want uncertainty resolved quickly—especially around costs, pathways, outcomes, and supports.

• Operational expectations are rising. Students expect continuity across touchpoints, not disconnected handoffs.

If students can’t get clear answers quickly, your website becomes verification—or a fallback—rather than the primary decision tool.

The real risk isn’t “AI adoption.” It’s disintermediation.

Many AI discussions in higher ed start with academic integrity. Important—yes. But the fastest institutional exposure for most leaders is about recruitment, trust, and operational load.

Recruitment influence moves upstream

If students form shortlists inside AI tools before reaching your site, traditional improvements (more content, prettier pages, stronger CTAs) have less leverage.

This is compounded by changing click behavior in search. Pew Research found that when Google users encountered an AI summary, they clicked a traditional search result in 8% of visits, versus 15% when no AI summary appeared. Clicking links inside AI summaries happened 1% of the time.

The institutions that win in AI-mediated discovery will be the ones that are easiest to understand and compare.

Inconsistency becomes visible—and costly

Large institutions often have strong content, but it’s described differently across faculties, departments, and service units. AI summarization makes those gaps harder to hide.

Inconsistency creates uncertainty—and uncertainty reduces applications, confidence, and trust. When institutional information conflicts across pages, AI systems don’t “fix” it—they can amplify contradictions at speed unless there’s a clear canonical source.

Fragmentation increases service demand

When students can’t find or interpret what they need, they contact people. Advising, enrolment services, international offices, financial aid, and housing absorb the cost of unclear digital experiences.

Clarity isn’t only “marketing.” It reduces repetitive questions and avoidable escalations.

What students are actually trying to do (and why websites struggle)

Prospective students rarely navigate by faculty structure. They navigate by uncertainty. A student may start with one program, but quickly needs answers across multiple domains.

Common decision questions:

• Admission and pathways: What if admission is competitive? What are realistic backups? How do transfers and switching work?

• Program planning: What’s a minor or double major? How flexible is the degree? What does first year look like?

• Cost and feasibility: What’s the true cost (tuition, fees, housing, living expenses)? How does financial aid work in practice?

• Outcomes and employability: What do graduates actually do? What co-op, internship, or placement pathways exist?

• Support and belonging: Housing, wellbeing, accessibility, advising quality, safety, community.

• International student needs: Visa requirements, work rules, language supports, arrival logistics, ongoing compliance.

These aren’t edge cases. They’re decision drivers.

Students don’t drop off because they lack interest. They drop off because uncertainty remains unresolved.

What students do when they can’t get clear answers

When navigation and internal search don’t work, students seek clarity elsewhere. Community sources feel more direct and more specific—especially for nuanced questions.

As AI tools become more common, students increasingly treat them as the place to ask:

• “What should I do if I don’t get into my first choice program?”

• “Can I afford this given my constraints?”

• “What pathway gives me the best chance at employability?”

This changes the role of the institutional website:

• from browsing-based exploration → to answer-based decision support

• from content volume → to clarity, consistency, and trust signals

• from “everything to everyone” → to clear pathways for real cohorts

Your advantage becomes authority and decisiveness—because that’s what both students and AI systems rely on when stakes are high.

The opportunity: AI can make institutions more coherent—if the foundations are right

AI doesn’t have to be framed as a threat. It can be the strongest forcing function in years to modernize core student journeys, improve findability, and reduce service friction. The highest-value opportunities fall into four categories.

1) Turn the website into a decision-support system

The future-state university website is not a library of pages. It’s a set of journeys that guide students through uncertainty with clear answers, tradeoffs, and next steps.

High-impact examples:

• Program comparison and pathway guidance (including alternatives when admission is competitive)

• Plain-language explanations of degree structures (minors, majors, switching)

• Cost guidance that reflects real scenarios, not just base tuition

• Outcome narratives that are specific (skills, pathways, alumni trajectories, employer signals)

• Cohort-specific journeys (international, transfer, mature, first-gen) that reduce dead ends

When students can complete decision journeys online, you reduce drop-off and improve applicant quality—not just volume.

2) Fix search and findability properly (before adding AI)

Adding layers—new menus, new microsites, or a chatbot—rarely fixes the underlying problem. The work that makes both search and AI reliable is structural:

• a shared taxonomy (content types and categorization)

• canonical sources of truth (one authoritative program page, one policy page)

• information architecture organized around student tasks, not internal ownership

• structured content and metadata for programs, requirements, costs, deadlines, services

• content governance (ownership, freshness, review cycles)

Search isn’t a UX enhancement. It’s the backbone of AI credibility and institutional trust.

3) Deploy AI-assisted guidance safely and credibly

Once foundations exist, AI can reduce friction without becoming a reputational risk.

Where AI creates real value:

• Admissions and program Q&A that cites official sources

• Guided exploration tools (“Based on your goals and constraints, here are realistic pathways”)

• Next-best-action guidance (apply, book advising, estimate costs, check prerequisites)

• Multilingual and accessibility support to improve equity of access

• Service triage that routes students to the right office with context intact

Safeguards that matter:

• source citation and links to canonical pages

• uncertainty handling and escalation to humans for high-stakes questions

• version control for deadlines, policies, and requirements

• governance and auditing

Safe AI is designed. It’s not purchased.

4) Modernize student communications across the lifecycle

The student experience is a multi-year relationship. AI can improve continuity across touchpoints such as:

• recruitment → admissions → onboarding

• advising → registration → program changes

• housing → financial aid → international student services

• accessibility → wellbeing → academic supports

• career services → internships → alumni pathways

This is often an alignment and information-flow problem, not a tooling problem.

When communications become coherent and proactive, student confidence rises and service burden falls.

A practical roadmap for higher ed leaders

A credible approach is rarely one “AI project.” It’s a phased program focused on clarity, structure, and measurable outcomes.

Phase 1: Diagnose the journeys that matter most

• Top decision journeys and where they break down

• The confusion points driving drop-off and service demand

• Web ecosystem mapping (ownership, duplication, contradictions)

• Search and IA assessment (taxonomy gaps, metadata gaps, canonical conflicts)

Phase 2: Build foundations for coherence

• Redesign IA around student tasks and decision moments

• Establish taxonomy, metadata standards, and governance

• Define canonical sources for programs, requirements, costs, services

• Rebuild priority journeys first (not everything at once)

  
  

Phase 3: Deploy AI where it creates measurable value

• On-site answer experiences grounded in authoritative sources

• Structured comparison tools and improved internal search

• AI-assisted communications and service triage with safeguards

The goal isn’t “AI everywhere.” The goal is clarity where it changes decisions and reduces friction.

How PH1 helps

PH1 supports higher education institutions that need change to work within real governance constraints. We help decision-makers move from broad AI interest to a credible roadmap that improves student experience, strengthens recruitment, and reduces institutional risk.

Our work commonly includes:

• Student and stakeholder research to surface decision drivers and trust gaps

• Journey mapping and service design across recruitment, admissions, and student services

• Information architecture and content systems to unify complex ecosystems and improve findability

• AI strategy and evaluation to design credible AI-assisted experiences institutions can govern

• Executive alignment and prioritization to secure cross-unit buy-in and sequence work realistically

We don’t help institutions “add AI.” We help them become clearer and more coherent—so AI improves the experience rather than exposing gaps.